Practical explainable AI using Python : artificial intelligence model explanations using Python-based libraries, extensions, and frameworks

Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks

Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as...

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Detalles Bibliográficos
Otros Autores: Mishra, Pradeepta, author (author)
Formato: Electrónico
Idioma:Inglés
Publicado: New York, New York : Apress [2021]
Materias:
Python (Computer program language)
Artificial intelligence > Data processing.
Application software > Development.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009644291806719
Tabla de Contenidos:
  • Chapter 1: Introduction to Model Explainability and Interpretability
  • Chapter 2: AI Ethics, Biasness and Reliability
  • Chapter 3: Model Explainability for Linear Models Using XAI Components
  • Chapter 4: Model Explainability for Non-Linear Models using XAI Components
  • Chapter 5: Model Explainability for Ensemble Models Using XAI Components
  • Chapter 6: Model Explainability for Time Series Models using XAI Components
  • Chapter 7: Model Explainability for Natural Language Processing using XAI Components
  • Chapter 8: AI Model Fairness Using What-If Scenario
  • Chapter 9: Model Explainability for Deep Neural Network Models
  • Chapter 10: Counterfactual Explanations for XAI models
  • Chapter 11: Contrastive Explanation for Machine Learning
  • Chapter 12: Model-Agnostic Explanations By Identifying Prediction Invariance
  • Chapter 13: Model Explainability for Rule based Expert System
  • Chapter 14: Model Explainability for Computer Vision.

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